Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu

Samponu, Yohakim Benedictus; Kusrini, Kusrini

doi:10.31961/eltikom.v1i2.29

Cited by 4 publications

(5 citation statements)

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“…The next stage is data cleaning. Unclean data refers to data containing impurities in the form of missing values [18]. Applying data mining to dirty data can yield less accurate results in the analysis process.…”

Section: Data Cleaningmentioning

confidence: 99%

Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Jannah,

Widodo,

Widodo

et al. 2024

SISFOKOM

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The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.

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Section: Data Cleaningmentioning

confidence: 99%

Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Jannah,

Widodo,

Widodo

et al. 2024

SISFOKOM

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“…Cross validation digunakan oleh beberapa peneliti digunakan untuk meningkatkan akurasi. Seperti yang dilakukan oleh (Samponu & Kusrini, 2018) menghasilkan akurasi 2% lebih tinggi dibandingan tidak menggunakan cross validation. Kemudian hasil temuan yang dilakukan oleh peneliti sebelumnya menyatakan bahwa bahwa penggunaan cross validation dapat meningkatkan nilai akurasi (Azis et al, 2020;Rhomadhona & Permadi, 2019;Ulfah & Anam, 2020).…”

Section: Pembahasanunclassified

Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

Efriadi¹,

Rahmaddeni²,

Agustin³

et al. 2022

Edumatic

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There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study.

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“…Karamouiz dan Vretoz [8] melakukan studi tentang indikator efektivitas lembaga pendidikan berdasarkan tingkat kelulusan siswa menggunakan artificial neural network (ANN). Prediksi tingkat kelulusan mahasiswa telah dilakukan menggunakan naive bayes dalam [9]- [11], C4.5 dalam [9], k-nearest neighbors (kNN) [12], algoritme apriori dalam [13], dan ANN [14]. Dengan beragam data kelulusan mahasiswa, akurasi prediksi naive bayes mencapai 80,85% [9] dan 80,72% [10], akurasi C4.5 sebesar 85,7% [9], akurasi kNN 80% [12].…”

Section: Pendahuluanunclassified

“…Hasil tersebut menunjukkan bahwa algoritme SVM memiliki kinerja yang baik dalam membangun sebuah model yang digunakan untuk menemukan informasi berdasarkan dengan dataset yang disesuaikan dengan tiga tahap evaluasi yang ada pada aturan akademik yaitu dua tahun pertama, dua tahun kedua dan akhir studi berdasarkan atribut yang telah ditentukan sebelumnya (Tabel 2). Informasi yang diperoleh tersebut memberikan pengetahuan untuk pemantauan evaluasi ketepatan masa studi mahasiswa di setiap fase evaluasi (Gambar 7), yang berbeda dengan [9], [15] yang hanya menggunakan dataset mahasiswa di satu tahun pertama, dengan [12] yang menggunakan dataset dua tahun pertama, dan dengan [11], [13], [14] yang menggunakan dataset mahasiswa yang telah menyelesaikan studinya. Mahasiswa dapat memperoleh ruang gerak yang lebih Penelitian ini tidak melakukan proses verifikasi, seperti dengan uji statistik, sebagaimana [15] yang melakukan verifikasi akurasi dengan uji T untuk menaikkan tingkat kepercayaan.…”

Section: Metode Penelitianunclassified

“…Mahasiswa dapat memperoleh ruang gerak yang lebih Penelitian ini tidak melakukan proses verifikasi, seperti dengan uji statistik, sebagaimana [15] yang melakukan verifikasi akurasi dengan uji T untuk menaikkan tingkat kepercayaan. Perbaikan kinerja masih dapat dilakukan dengan melakukan proses penalaan parameter pada proses validasi, sebagaimana [11], [12] untuk memperoleh nilai akurasi prediksi terbaik atau mempersiapkan data menggunakan metode cross-tabulation seperti [14].…”

Section: Metode Penelitianunclassified

See 1 more Smart Citation

Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness

Fadli

Zulfa

Ramadhani

2018

Jurnal Teknologi dan Sistem Komputer

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Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.

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Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu

Abstract: Education at this time is an important requirement in facing the demands of an increasingly advanced era in

Cited by 4 publications

References 0 publications

Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness

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